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dc.contributor.authorLiu, Yi
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2017-03-03T19:55:08Z
dc.date.available2017-03-03T19:55:08Z
dc.date.issued2016
dc.identifier.citationLiu Y, Wang P. Probabilistic Modeling and Analysis of Fused Deposition Modeling Process Using Surrogate Models. ASME. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 2B: 42nd Design Automation Conference ():V02BT03A051en_US
dc.identifier.isbn978-0-7918-5011-4
dc.identifier.otherWOS:000393363400051
dc.identifier.urihttp://dx.doi.org/10.1115/DETC2016-59603
dc.identifier.urihttp://hdl.handle.net/10057/12890
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractVarious sources of uncertain parameters at multiple levels, from design steps to manufacturing processes, are often involved in composite structures. Probabilistic modeling and analysis of the composite structure and its manufacturing processes can provide underlying information to assess uncertainties and improve the quality of the developed composite structures. This paper presents a stochastic multi-level modeling framework considering material, structural, modeling parameters as well as the manufacturing process based on a surrogate model. An enhanced laminate theory is employed to determine the elastic constants of the composite materials considering imperfect bonding among filaments in the manufacturing process. To improve the computational efficiency in simulation-based reliability approach, the evaluation of the structure properties is approximated by employing surrogate models based upon the physics model. To apply the present framework, a case study with a composite laminate beam under three-point bending, which is made through fused deposition modeling, is conducted, and the case study results demonstrate the efficacy of the presented modeling scheme and analysis methodology.en_US
dc.description.sponsorshipNational Science Foundation through Faculty Early Career Development (CAREER) award (CMMI-1351414) and the Award (CMMI-111538508).en_US
dc.language.isoen_USen_US
dc.publisherAmerican Society of Mechanical Engineersen_US
dc.relation.ispartofseriesASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference;v.2B
dc.subjectUncertaintyen_US
dc.subjectOptimizationen_US
dc.subjectCompositesen_US
dc.subjectPolymeren_US
dc.titleProbabilistic modeling and analysis of fused deposition modeling process using surrogate modelsen_US
dc.typeConference paperen_US
dc.rights.holder© 2016 by ASMEen_US


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